时间：7月18日（周三） 14：30-16：00
地点：明德主楼 1016 会议室
题目：Cross-Validation for Optimal and Reproducible Statistical Learning
摘要：In data mining and statistical learning, we frequently encounter the task of comparing different methods/algorithms to reach a final choice for pure prediction or a scientific understanding / interpretation of a regression relationship. Cross-validation provides a powerful tool to address the matter. Unfortunately, there are seemingly widespread misconceptions on its use, which can lead to unreliable conclusions. In this talk, we will address the subtle issues involved and present results of minimax optimal regression learning and consistent selection of the best method for the data. In addition, we will propose proper cross-validation tools for model selection diagnostics that will cry foul at an impressive-looking but not really reproducible outcome from a sparse-pattern-hunting method in the wild west of learning with a huge number of covariates.
简介：Yuhong Yang received his Ph.D from Yale in statistics in 1996. He then joined the Department of Statistics at Iowa State University and moved to the University of Minnesota in 2004. He has been a full professor there since 2007. His research interests include model selection, multi-armed bandit problems, forecasting, high-dimensional data analysis, and machine learning. He has published in journals in several fields, including Annals of Statistics, JRSSB, JASA, Biometrika, IEEE Transaction on Information Theory, Journal of Econometrics, Journal of Approximation Theory, Journal of Machine Learning Research, and International Journal of Forecasting. He is a fellow of Institute of Mathematical Statistics.